InicioTesis

REDDA - Red de Depósito Atmosférico

# RED DE DEPÓSITO ATMOSFÉRICO - REDDA

# 2010

# redda10_A <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010CAH.xls", sheet = 1, header = TRUE)
# redda10_B <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010CEH.xls", sheet = 1, header = TRUE)
# redda10_C <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010CLH.xls", sheet = 1, header = TRUE)
# redda10_D <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010HH.xls", sheet = 1, header = TRUE)
# redda10_E <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010KH.xls", sheet = 1, header = TRUE)
# redda10_F <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010MGH.xls", sheet = 1, header = TRUE)
# redda10_G <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010NAH.xls", sheet = 1, header = TRUE)
# redda10_H <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010NH4H.xls", sheet = 1, header = TRUE)
# redda10_I <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010NO3H.xls", sheet = 1, header = TRUE)
# redda10_J <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010PHH.xls", sheet = 1, header = TRUE)
# redda10_K <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010PPH.xls", sheet = 1, header = TRUE)
# redda10_L <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/10REDDA/2010SO4H.xls", sheet = 1, header = TRUE)
# 
# redda10_A_tidy <- redda10_A %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CAH")
# redda10_B_tidy <- redda10_B %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CEH")
# redda10_C_tidy <- redda10_C %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CLH")
# redda10_D_tidy <- redda10_D %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "HH")
# redda10_E_tidy <- redda10_E %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "KH")
# redda10_F_tidy <- redda10_F %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "MGH")
# redda10_G_tidy <- redda10_G %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NAH")
# redda10_H_tidy <- redda10_H %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NA4H")
# redda10_I_tidy <- redda10_I %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NO3H")
# redda10_J_tidy <- redda10_J %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PH")
# redda10_K_tidy <- redda10_K %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PPH")
# redda10_L_tidy <- redda10_L %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "SO4H")
# 
# redda10 <- Reduce(function(...) merge(..., all=T), list(redda10_A_tidy, redda10_B_tidy, redda10_C_tidy, redda10_D_tidy, redda10_E_tidy, redda10_F_tidy, redda10_G_tidy, redda10_H_tidy, redda10_I_tidy, redda10_J_tidy, redda10_K_tidy, redda10_L_tidy))
# 
# # 2011
# 
# redda11_A <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011CAH.xls", sheet = 1, header = TRUE)
# redda11_B <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011CEH.xls", sheet = 1, header = TRUE)
# redda11_C <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011CLH.xls", sheet = 1, header = TRUE)
# redda11_D <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011HH.xls", sheet = 1, header = TRUE)
# redda11_E <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011KH.xls", sheet = 1, header = TRUE)
# redda11_F <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011MGH.xls", sheet = 1, header = TRUE)
# redda11_G <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011NAH.xls", sheet = 1, header = TRUE)
# redda11_H <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011NH4H.xls", sheet = 1, header = TRUE)
# redda11_I <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011NO3H.xls", sheet = 1, header = TRUE)
# redda11_J <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011PHH.xls", sheet = 1, header = TRUE)
# redda11_K <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011PPH.xls", sheet = 1, header = TRUE)
# redda11_L <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/11REDDA/2011SO4H.xls", sheet = 1, header = TRUE)
# 
# 
# redda11_A_tidy <- redda11_A %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CAH")
# redda11_B_tidy <- redda11_B %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CEH")
# redda11_C_tidy <- redda11_C %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CLH")
# redda11_D_tidy <- redda11_D %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "HH")
# redda11_E_tidy <- redda11_E %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "KH")
# redda11_F_tidy <- redda11_F %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "MGH")
# redda11_G_tidy <- redda11_G %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NAH")
# redda11_H_tidy <- redda11_H %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NA4H")
# redda11_I_tidy <- redda11_I %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NO3H")
# redda11_J_tidy <- redda11_J %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PH")
# redda11_K_tidy <- redda11_K %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PPH")
# redda11_L_tidy <- redda11_L %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "SO4H")
# 
# redda11 <- Reduce(function(...) merge(..., all=T), list(redda11_A_tidy, redda11_B_tidy, redda11_C_tidy, redda11_D_tidy, redda11_E_tidy, redda11_F_tidy, redda11_G_tidy, redda11_H_tidy, redda11_I_tidy, redda11_J_tidy, redda11_K_tidy, redda11_L_tidy))
# 
# # 2012
# 
# redda12_A <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012CAH.xls", sheet = 1, header = TRUE)
# redda12_B <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012CEH.xls", sheet = 1, header = TRUE)
# redda12_C <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012CLH.xls", sheet = 1, header = TRUE)
# redda12_D <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012HH.xls", sheet = 1, header = TRUE)
# redda12_E <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012KH.xls", sheet = 1, header = TRUE)
# redda12_F <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012MGH.xls", sheet = 1, header = TRUE)
# redda12_G <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012NAH.xls", sheet = 1, header = TRUE)
# redda12_H <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012NH4H.xls", sheet = 1, header = TRUE)
# redda12_I <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012NO3H.xls", sheet = 1, header = TRUE)
# redda12_J <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012PHH.xls", sheet = 1, header = TRUE)
# redda12_K <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012PPH.xls", sheet = 1, header = TRUE)
# redda12_L <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/12REDDA/2012SO4H.xls", sheet = 1, header = TRUE)
# 
# redda12_A_tidy <- redda12_A %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CAH")
# redda12_B_tidy <- redda12_B %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CEH")
# redda12_C_tidy <- redda12_C %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CLH")
# redda12_D_tidy <- redda12_D %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "HH")
# redda12_E_tidy <- redda12_E %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "KH")
# redda12_F_tidy <- redda12_F %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "MGH")
# redda12_G_tidy <- redda12_G %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NAH")
# redda12_H_tidy <- redda12_H %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NA4H")
# redda12_I_tidy <- redda12_I %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NO3H")
# redda12_J_tidy <- redda12_J %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PH")
# redda12_K_tidy <- redda12_K %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PPH")
# redda12_L_tidy <- redda12_L %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "SO4H")
# 
# redda12 <- Reduce(function(...) merge(..., all=T), list(redda12_A_tidy, redda12_B_tidy, redda12_C_tidy, redda12_D_tidy, redda12_E_tidy, redda12_F_tidy, redda12_G_tidy, redda12_H_tidy, redda12_I_tidy, redda12_J_tidy, redda12_K_tidy, redda12_L_tidy))
# 
# # 2013
# 
# redda13_A <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013CAH.xls", sheet = 1, header = TRUE)
# redda13_B <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013CEH.xls", sheet = 1, header = TRUE)
# redda13_C <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013CLH.xls", sheet = 1, header = TRUE)
# redda13_D <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013HH.xls", sheet = 1, header = TRUE)
# redda13_E <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013KH.xls", sheet = 1, header = TRUE)
# redda13_F <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013MGH.xls", sheet = 1, header = TRUE)
# redda13_G <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013NAH.xls", sheet = 1, header = TRUE)
# redda13_H <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013NH4H.xls", sheet = 1, header = TRUE)
# redda13_I <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013NO3H.xls", sheet = 1, header = TRUE)
# redda13_J <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013PHH.xls", sheet = 1, header = TRUE)
# redda13_K <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013PPH.xls", sheet = 1, header = TRUE)
# redda13_L <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/13REDDA/2013SO4H.xls", sheet = 1, header = TRUE)
# 
# redda13_A_tidy <- redda13_A %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CAH")
# redda13_B_tidy <- redda13_B %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CEH")
# redda13_C_tidy <- redda13_C %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CLH")
# redda13_D_tidy <- redda13_D %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "HH")
# redda13_E_tidy <- redda13_E %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "KH")
# redda13_F_tidy <- redda13_F %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "MGH")
# redda13_G_tidy <- redda13_G %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NAH")
# redda13_H_tidy <- redda13_H %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NA4H")
# redda13_I_tidy <- redda13_I %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NO3H")
# redda13_J_tidy <- redda13_J %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PH")
# redda13_K_tidy <- redda13_K %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PPH")
# redda13_L_tidy <- redda13_L %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "SO4H")
# 
# redda13 <- Reduce(function(...) merge(..., all=T), list(redda13_A_tidy, redda13_B_tidy, redda13_C_tidy, redda13_D_tidy, redda13_E_tidy, redda13_F_tidy, redda13_G_tidy, redda13_H_tidy, redda13_I_tidy, redda13_J_tidy, redda13_K_tidy, redda13_L_tidy))
# 
# # 2014
# 
# redda14_A <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014CAH.xls", sheet = 1, header = TRUE)
# redda14_B <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014CEH.xls", sheet = 1, header = TRUE)
# redda14_C <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014CLH.xls", sheet = 1, header = TRUE)
# redda14_D <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014HH.xls", sheet = 1, header = TRUE)
# redda14_E <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014KH.xls", sheet = 1, header = TRUE)
# redda14_F <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014MGH.xls", sheet = 1, header = TRUE)
# redda14_G <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014NAH.xls", sheet = 1, header = TRUE)
# redda14_H <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014NH4H.xls", sheet = 1, header = TRUE)
# redda14_I <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014NO3H.xls", sheet = 1, header = TRUE)
# redda14_J <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014PHH.xls", sheet = 1, header = TRUE)
# redda14_K <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014PPH.xls", sheet = 1, header = TRUE)
# redda14_L <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/14REDDA/2014SO4H.xls", sheet = 1, header = TRUE)
# 
# redda14_A_tidy <- redda14_A %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CAH")
# redda14_B_tidy <- redda14_B %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CEH")
# redda14_C_tidy <- redda14_C %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CLH")
# redda14_D_tidy <- redda14_D %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "HH")
# redda14_E_tidy <- redda14_E %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "KH")
# redda14_F_tidy <- redda14_F %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "MGH")
# redda14_G_tidy <- redda14_G %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NAH")
# redda14_H_tidy <- redda14_H %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NA4H")
# redda14_I_tidy <- redda14_I %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NO3H")
# redda14_J_tidy <- redda14_J %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PH")
# redda14_K_tidy <- redda14_K %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PPH")
# redda14_L_tidy <- redda14_L %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "SO4H")
# 
# redda14 <- Reduce(function(...) merge(..., all=T), list(redda14_A_tidy, redda14_B_tidy, redda14_C_tidy, redda14_D_tidy, redda14_E_tidy, redda14_F_tidy, redda14_G_tidy, redda14_H_tidy, redda14_I_tidy, redda14_J_tidy, redda14_K_tidy, redda14_L_tidy))
# 
# # 2015
# 
# redda15_A <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015CAH.xls", sheet = 1, header = TRUE)
# redda15_B <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015CEH.xls", sheet = 1, header = TRUE)
# redda15_C <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015CLH.xls", sheet = 1, header = TRUE)
# redda15_D <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015HH.xls", sheet = 1, header = TRUE)
# redda15_E <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015KH.xls", sheet = 1, header = TRUE)
# redda15_F <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015MGH.xls", sheet = 1, header = TRUE)
# redda15_G <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015NAH.xls", sheet = 1, header = TRUE)
# redda15_H <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015NH4H.xls", sheet = 1, header = TRUE)
# redda15_I <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015NO3H.xls", sheet = 1, header = TRUE)
# redda15_J <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015PHH.xls", sheet = 1, header = TRUE)
# redda15_K <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015PPH.xls", sheet = 1, header = TRUE)
# redda15_L <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/15REDDA/2015SO4H.xls", sheet = 1, header = TRUE)
# 
# redda15_A_tidy <- redda15_A %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CAH")
# redda15_B_tidy <- redda15_B %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CEH")
# redda15_C_tidy <- redda15_C %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CLH")
# redda15_D_tidy <- redda15_D %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "HH")
# redda15_E_tidy <- redda15_E %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "KH")
# redda15_F_tidy <- redda15_F %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "MGH")
# redda15_G_tidy <- redda15_G %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NAH")
# redda15_H_tidy <- redda15_H %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NA4H")
# redda15_I_tidy <- redda15_I %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NO3H")
# redda15_J_tidy <- redda15_J %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PH")
# redda15_K_tidy <- redda15_K %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PPH")
# redda15_L_tidy <- redda15_L %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "SO4H")
# 
# redda15 <- Reduce(function(...) merge(..., all=T), list(redda15_A_tidy, redda15_B_tidy, redda15_C_tidy, redda15_D_tidy, redda15_E_tidy, redda15_F_tidy, redda15_G_tidy, redda15_H_tidy, redda15_I_tidy, redda15_J_tidy, redda15_K_tidy, redda15_L_tidy))
# 
# # 2016
# 
# redda16_A <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016CAH.xls", sheet = 1, header = TRUE)
# redda16_B <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016CEH.xls", sheet = 1, header = TRUE)
# redda16_C <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016CLH.xls", sheet = 1, header = TRUE)
# redda16_D <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016HH.xls", sheet = 1, header = TRUE)
# redda16_E <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016KH.xls", sheet = 1, header = TRUE)
# redda16_F <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016MGH.xls", sheet = 1, header = TRUE)
# redda16_G <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016NAH.xls", sheet = 1, header = TRUE)
# redda16_H <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016NH4H.xls", sheet = 1, header = TRUE)
# redda16_I <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016NO3H.xls", sheet = 1, header = TRUE)
# redda16_J <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016PHH.xls", sheet = 1, header = TRUE)
# redda16_K <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016PPH.xls", sheet = 1, header = TRUE)
# redda16_L <- read.xls("/Users/Vanessa/Desktop/Datos_Excel/REDDA/16REDDA/2016SO4H.xls", sheet = 1, header = TRUE)
# 
# redda16_A_tidy <- redda16_A %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CAH")
# redda16_B_tidy <- redda16_B %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CEH")
# redda16_C_tidy <- redda16_C %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "CLH")
# redda16_D_tidy <- redda16_D %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "HH")
# redda16_E_tidy <- redda16_E %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "KH")
# redda16_F_tidy <- redda16_F %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "MGH")
# redda16_G_tidy <- redda16_G %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NAH")
# redda16_H_tidy <- redda16_H %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NA4H")
# redda16_I_tidy <- redda16_I %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "NO3H")
# redda16_J_tidy <- redda16_J %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PH")
# redda16_K_tidy <- redda16_K %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "PPH")
# redda16_L_tidy <- redda16_L %>% gather(estaciones, valor, LOM:LAA, -FECHA) %>% mutate(pollutant = "SO4H")
# 
# redda16 <- Reduce(function(...) merge(..., all=T), list(redda16_A_tidy, redda16_B_tidy, redda16_C_tidy, redda16_D_tidy, redda16_E_tidy, redda16_F_tidy, redda16_G_tidy, redda16_H_tidy, redda16_I_tidy, redda16_J_tidy, redda16_K_tidy, redda16_L_tidy))

# NO HAY 2017

# redda <- rbind(redda10, redda11, redda13, redda14, redda15, redda16) # Arreglar REDDA 12
# redda$FECHA <- as.Date(redda$FECHA)

redda <- read.csv("/Users/Vanessa/Desktop/Datos_Excel/redda.csv")
# write_csv(redda, "/Users/Vanessa/Desktop/Datos_Excel/redda.csv")

# %>% mutate(imeca = convert_to_imeca(valor, pollutant = pollutant, showWarnings = F)) 
write_csv(redda, "/Users/Vanessa/Desktop/CSV/redda.csv")

Serie de Tiempo por Pollutant (17) con REDDA

reddag <- ggplotly(ggplot(redda, aes(x = FECHA, y = valor, color = estaciones)) + geom_line() + facet_grid(pollutant ~ .) + theme_bw() )

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

reddag

reddag2 <- ggplotly(ggplot(redda, aes(x = FECHA, y = valor, color = pollutant)) + geom_line() + facet_grid(estaciones ~ .) + theme_bw() )

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

reddag2

REDMA

Limpieza y orden datos REDMA

Se cargan todos los dastos de REDMA como RDS desde el 2010 hasta 2017 unicamente con las estaciones: MER; PED; XAL

redma <- readRDS("/Users/Vanessa/Desktop/TESIS/DatosParaTrabajar/REDMA/redma.rds")
write_csv(redma, "/Users/Vanessa/Desktop/CSV/redma.csv")

REDMET - Red de meteorología y radiacción (TMP, WDR, WSP, RH)

Limpieza y orden datos REDMET (2010-2017, Estaciones:TLA MER SAG, Variables: TMP; WDR, WSP, RH)

Se cargan todos los dastos de REDMET como RDS desde el 2010 hasta 2017 unicamente con las estaciones: TLA MER SAG

redmet10 <- readRDS("/Users/Vanessa/Desktop/Datos_Excel/REDMET/redmet10.rds")
redmet11 <- readRDS("/Users/Vanessa/Desktop/Datos_Excel/REDMET/redmet11.rds")
redmet12 <- readRDS("/Users/Vanessa/Desktop/Datos_Excel/REDMET/redmet12.rds")
redmet13 <- readRDS("/Users/Vanessa/Desktop/Datos_Excel/REDMET/redmet13.rds")
redmet14 <- readRDS("/Users/Vanessa/Desktop/Datos_Excel/REDMET/redmet14.rds")
redmet15 <- readRDS("/Users/Vanessa/Desktop/Datos_Excel/REDMET/redmet15.rds")
redmet16 <- readRDS("/Users/Vanessa/Desktop/Datos_Excel/REDMET/redmet16.rds")
redmet17 <- readRDS("/Users/Vanessa/Desktop/Datos_Excel/REDMET/redmet17.rds")

redmet14 <- redmet14[,c(1,2,13,14,15)]
redmet15 <- redmet15[,-3]
redmet16 <- redmet16[,-c(3,4)]
redemt17 <- redmet17[,-c(3,4)]
redmet <- rbind(redmet10, redmet11, redmet12, redmet13, redmet14, redmet15, redmet16, redemt17)
# clean.envir("redmet1")
saveRDS(redmet, "/Users/Vanessa/Desktop/TESIS/DatosParaTrabajar/REDMET/redmet.rds")
write_csv(redmet, "/Users/Vanessa/Desktop/CSV/redmet.csv")

Formatear datos para hacer graficas de windRose

redmet10_wide <- redmet10 %>% group_by(FECHA, pollutant) %>% #pollutant en este data frame es para indicar parametro: windspeed, relative humidity, wind direction, or temperastura
                          summarise(max_value = max(valor, na.rm = TRUE))  %>% 
                          spread(pollutant, max_value)

redmet10_wide$FECHA <- as.Date(redmet10_wide$FECHA)

names(redmet10_wide)[1] <- "date" 

# 2011
redmet11_wide <- redmet11 %>% group_by(FECHA, pollutant) %>% #pollutant en este data frame es para indicar parametro: windspeed, relative humidity, wind direction, or temperastura
                          summarise(max_value = max(valor, na.rm = TRUE))  %>% 
                          spread(pollutant, max_value)

redmet11_wide$FECHA <- as.Date(redmet11_wide$FECHA)
names(redmet11_wide)[1] <- "date" 

# 2012
redmet12_wide <- redmet12 %>% group_by(FECHA, pollutant) %>% #pollutant en este data frame es para indicar parametro: windspeed, relative humidity, wind direction, or temperastura
                          summarise(max_value = max(valor, na.rm = TRUE))  %>% 
                          spread(pollutant, max_value)

redmet12_wide$FECHA <- as.Date(redmet12_wide$FECHA)
names(redmet12_wide)[1] <- "date" 

# 2013
redmet13_wide <- redmet13 %>% group_by(FECHA, pollutant) %>% #pollutant en este data frame es para indicar parametro: windspeed, relative humidity, wind direction, or temperastura
                          summarise(max_value = max(valor, na.rm = TRUE))  %>% 
                          spread(pollutant, max_value)

redmet13_wide$FECHA <- as.Date(redmet13_wide$FECHA)
names(redmet13_wide)[1] <- "date" 

# 2014
redmet14_wide <- redmet14 %>% group_by(FECHA, pollutant) %>% #pollutant en este data frame es para indicar parametro: windspeed, relative humidity, wind direction, or temperastura
                          summarise(max_value = max(valor, na.rm = TRUE))  %>% 
                          spread(pollutant, max_value)

redmet14_wide$FECHA <- as.Date(redmet14_wide$FECHA)
names(redmet14_wide)[1] <- "date" 

# 2015
redmet15_wide <- redmet15 %>% group_by(FECHA, pollutant) %>% #pollutant en este data frame es para indicar parametro: windspeed, relative humidity, wind direction, or temperastura
                          summarise(max_value = max(valor, na.rm = TRUE))  %>% 
                          spread(pollutant, max_value)

redmet15_wide$FECHA <- as.Date(redmet15_wide$FECHA)
names(redmet15_wide)[1] <- "date" 

# 2016
redmet16_wide <- redmet16 %>% group_by(FECHA, pollutant) %>% #pollutant en este data frame es para indicar parametro: windspeed, relative humidity, wind direction, or temperastura
                          summarise(max_value = max(valor, na.rm = TRUE))  %>% 
                          spread(pollutant, max_value)

redmet16_wide$FECHA <- as.Date(redmet16_wide$FECHA)
names(redmet16_wide)[1] <- "date" 

# 2017
redmet17_wide <- redmet17 %>% group_by(FECHA, pollutant) %>% #pollutant en este data frame es para indicar parametro: windspeed, relative humidity, wind direction, or temperastura
                          summarise(max_value = max(valor, na.rm = TRUE))  %>% 
                          spread(pollutant, max_value)

redmet17_wide$FECHA <- as.Date(redmet17_wide$FECHA)
names(redmet17_wide)[1] <- "date" 

Gráficas viento REDMET

data <- data.frame("Speed" = mydata$ws, "Direction"=mydata$wd)
data2 <- data.frame("ws"=data$Speed, "wd"=data$Direction)
windRose(data2)

data_wide10 <- redmet10_wide[ ,c("WDR", "WSP")] %>% mutate(ws = WSP, wd = WDR) %>% select(-c(WDR:WSP))
windRose(data_wide10)

data_wide11 <- redmet11_wide[ ,4:5] %>% mutate(ws = WSP, wd = WDR) %>% select(-c(WDR:WSP))
# names(data2) <- c("ws", "wd")
windRose(data_wide11)

data_wide12 <- redmet12_wide[ ,4:5] %>% mutate(ws = WSP, wd = WDR) %>% select(-c(WDR:WSP))
# names(data2) <- c("ws", "wd")
windRose(data_wide12)

data_wide13 <- redmet13_wide[ ,4:5] %>% mutate(ws = WSP, wd = WDR) %>% select(-c(WDR:WSP))
# names(data2) <- c("ws", "wd")
windRose(data_wide13)

data_wide14 <- redmet14_wide[ ,4:5] %>% mutate(ws = WSP, wd = WDR) %>% select(-c(WDR:WSP))
# names(data2) <- c("ws", "wd")
windRose(data_wide14)

data_wide15 <- redmet15_wide[ ,4:5] %>% mutate(ws = WSP, wd = WDR) %>% select(-c(WDR:WSP))
# names(data2) <- c("ws", "wd")
windRose(data_wide15)

data_wide16 <- redmet16_wide[ ,4:5] %>% mutate(ws = WSP, wd = WDR) %>% select(-c(WDR:WSP))
# names(data2) <- c("ws", "wd")
windRose(data_wide16)

data_wide17 <- redmet17_wide[ ,4:5] %>% mutate(ws = WSP, wd = WDR) %>% select(-c(WDR:WSP))
# names(data2) <- c("ws", "wd")
windRose(data_wide17)

RAMA

Limpieza y orden datos RAMA (2010-2017, Estaciones: TLA MER SAG, Pollutants)

Se cargan los datos de RAMA como RDS desde el 2010 hasta 2017 unicamente con las estaciones: TLA MER SAG

rama <- readRDS("/Users/Vanessa/Desktop/TESIS/DatosParaTrabajar/RAMA/rama.rds")

Gráficas de Calendario 2010-2017 de O3 (TLA, MER, SAG)

rama_wide <- rama %>% group_by(FECHA, pollutant) %>% 
                          summarise(max_value = max(valor, na.rm = TRUE))  %>% 
                          spread(pollutant, max_value)

rama_wide$FECHA <- as.Date(rama_wide$FECHA)
names(rama_wide)[1] <- "date" 
summaryPlot(rama_wide, percentile = 0.98, na.len = 12, xlab = c("evolucion anual", "Histogramas"), main="Resumen contaminantes")

##      date        CO        NO       NOX        O3      PM10      PM25 
##    "Date" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
##       SO2 
## "numeric"

## Warning in checkPrep(mydata, vars, type = "default", remove.calm = FALSE, :
## Detected data with Daylight Saving Time, converting to UTC/GMT

summaryPlot(rama_wide)

##      date        CO        NO       NOX        O3      PM10      PM25 
##    "Date" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
##       SO2 
## "numeric"

## Warning in checkPrep(mydata, vars, type = "default", remove.calm = FALSE, :
## Detected data with Daylight Saving Time, converting to UTC/GMT

purrr::walk(2010:2017, function(x) calendarPlot(rama_wide, pollutant = "O3", year = x))

Gráficas por hora - 01/01/10

Contaminantes por hora 01/01/10 (TLA, MER, SAG)

# dia <- as.Date("01/01/10")
# un_dia <- filter(rama, )

# un_dia %>% select(rama$FECHA, rama$HORA, rama$estaciones, rama$valor, rama$pollutant) %>% filter(rama$FECHA == as.Date("01/01/10"))


# un_dia  <- read.csv("/Users/Vanessa/Desktop/CSV/un_dia.csv")
# un_dia_plot <- ggplot(un_dia, aes(x = HORA, y = valor, color = pollutant, alpha = 0.005)) + geom_point() + geom_smooth() + theme_bw() # + facet_grid(estaciones) 
# un_dia_plot
# 
# un_dia_plot_line <- ggplot(un_dia, aes( x = HORA, y = valor, color = pollutant, alpha  = 0.005)) + geom_line() + theme_bw()
# un_dia_plot_line


solo_un_dia  <- read.csv("/Users/Vanessa/Desktop/CSV/solo_un_dia.csv")
dia_plot <- ggplot(solo_un_dia, aes(x = HORA, y = valor, color = pollutant, alpha = 0.005)) + geom_point() + geom_smooth() + theme_bw() # + facet_grid(estaciones) 
dia_plot

## `geom_smooth()` using method = 'loess'

dia_plot_line <- ggplot(solo_un_dia, aes( x = HORA, y = valor, color = pollutant, alpha  = 0.005)) + geom_line() + theme_bw()
dia_plot_line

ene1_10 <- read.csv("/Users/Vanessa/Desktop/CSV/01-01-10.csv")
d_plot <- ggplot(ene1_10, aes(x = HORA, y = valor, color = pollutant, alpha = 0.005)) + geom_line() + theme_bw()
d_plot

Estación MER - 01/01/2010

mer <- read.csv("/Users/Vanessa/Desktop/CSV/MER.csv")
mer_plot <- ggplot(mer, aes(x=HORA, y=valor, color=pollutant, alpha=0.05)) + geom_point() + geom_smooth() + theme_bw()
mer_plot

## `geom_smooth()` using method = 'loess'

mer_plot_line <- ggplot(mer, aes(x=HORA, y=valor, color=pollutant, alpha=0.05)) + geom_line() + theme_bw()
mer_plot_line

Estación SAG - 01/01/2010

sag  <- read.csv("/Users/Vanessa/Desktop/CSV/SAG.csv")
sag_plot <- ggplot(sag, aes(x=HORA, y=valor, color=pollutant, alpha=0.05)) + geom_point() + geom_smooth() + theme_bw()
sag_plot

## `geom_smooth()` using method = 'loess'

sag_plot_line <- ggplot(sag, aes(x=HORA, y=valor, color=pollutant, alpha=0.05)) + geom_line() + theme_bw()
sag_plot_line

Estación TLA - 01/01/2010

tla <- read.csv("/Users/Vanessa/Desktop/CSV/TLA.csv")
tla_plot <- ggplot(tla, aes(x=HORA, y=valor, color=pollutant, alpha=0.05)) + geom_point() + geom_smooth() + theme_bw()
tla_plot

## `geom_smooth()` using method = 'loess'

tla_plot_line <- ggplot(tla, aes(x=HORA, y=valor, color=pollutant, alpha=0.05)) + geom_line() + theme_bw()
tla_plot_line

Velocidad viento (WDS) - 01/01/10 (TLA, MER, SAG)

wds <- ggplot(ene1_10, aes(x = HORA, y = WDS, color = estaciones, alpha = 0.005)) + geom_line() + theme_bw()
wds

Serie de Tiempo 10-17 Máximos O3 (TLA, MER, SAG)

# library(lubridate)
# rama[1,1] %>% as.Date %>% year


# extraer el año para crear grupos para la serie de tiempo sin tener que modificar FECHA a formato de fecha
rama_wide <- rama %>% filter(pollutant == "O3") %>% group_by(FECHA) %>%
                          summarise(max_value = mean(valor, na.rm = TRUE)) %>% 
                          mutate(YEAR = substr(FECHA, 1, 4), MONTH = FECHA)

rama_wide$YEAR <- rama_wide$FECHA %>% substr(1, 4)
rama_wide$MONTH <- rama_wide$FECHA %>% substr(6, 7)
rama_wide$DAY <- c(rep(1:365, 2), 1:366, rep(1:365, 3), 1:366, 1:365)
  
ggplot(rama_wide, aes(x = DAY, y = max_value, col = YEAR)) + geom_line()

library(dygraphs)
library(xts)

## Warning: package 'xts' was built under R version 3.4.4

## Loading required package: zoo

## Warning: package 'zoo' was built under R version 3.4.3

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## 
## Attaching package: 'xts'

## The following objects are masked from 'package:dplyr':
## 
##     first, last

## The following objects are masked from 'package:gdata':
## 
##     first, last

library(dplyr)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

no_feb_29 <- !grepl("02-29", rama_wide$FECHA)


rama_wide$MONTH <- as.Date(rama_wide$FECHA) %>% month() %>% as.Date()



don_wide <- rama_wide[no_feb_29, c("MONTH", "YEAR", "max_value", "DAY")] %>% spread(YEAR, max_value)

fechas <- seq(as.Date('2015-01-01'), as.Date('2015-12-31'), by='1 day')

don <- xts(x = don_wide[1:365,-c(1:2)], order.by = fechas) 

dim(don_wide)

## [1] 375  10

length(fechas)

## [1] 365

widg = dygraph(don) %>%
  dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors="#D8AE5A") %>%
  dyRangeSelector() %>%
  dyCrosshair(direction = "vertical") %>%
  dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE)  %>%
  dyRoller(rollPeriod = 1)

widg

g <- dygraph(don, ylab = "max_value",
             main = "time series") %>% 
            dyRangeSelector()

g

Dias de máximo nivel de O3 - TLA MER SAG

Los días con los valores más altos de ozono registrados:

# date1 <- as.Date("2010-03-01")
# date2 <- as.Date("2010-05-31")
# 
# dates <- seq(date1, date1, by="days")
# 
# test <-subset(rama, FECHA %in% dates) 
# test <- filter(rama, filter(rama$FECHA>date1 & rama$FECHA<date2))
# 
# test <- rama[rama$FECHA >= date1 & < date2, ]
# test <- grep("O3", rama$pollutant)

rama$FECHA <- as.Date(rama$FECHA)
rama <- rama[rama$valor >= 0, ]
r <- rama[80257:99984,]
sort(r$valor, decreasing = T)

##     [1] 433 209 199 195 194 185 177 176 174 173 172 171 170 169 167 167 167
##    [18] 162 162 162 161 160 158 158 157 154 154 154 153 151 151 150 150 150
##    [35] 150 149 149 149 149 147 147 147 146 146 146 146 146 146 145 145 145
##    [52] 145 144 144 143 143 142 142 142 142 141 141 141 140 140 139 139 139
##    [69] 137 137 136 136 136 136 135 135 135 135 135 135 134 134 134 134 134
##    [86] 134 133 133 133 132 131 131 131 131 131 130 130 130 130 130 130 130
##   [103] 129 129 129 129 129 129 129 128 128 128 128 128 128 127 127 127 127
##   [120] 127 126 126 126 126 126 126 126 126 126 125 125 125 124 124 124 124
##   [137] 124 123 123 123 123 123 123 123 123 123 123 123 123 123 123 122 122
##   [154] 122 122 122 122 122 122 122 122 121 121 121 121 121 121 121 121 121
##   [171] 121 121 121 120 120 120 120 120 120 120 120 120 120 119 119 119 119
##   [188] 119 119 119 119 119 119 119 118 118 118 118 118 118 117 117 117 117
##   [205] 117 117 117 117 117 117 117 117 117 116 116 116 116 116 116 116 116
##   [222] 116 116 116 116 115 115 115 115 115 115 115 115 115 115 115 115 115
##   [239] 115 115 114 114 114 114 114 114 114 114 114 114 114 114 114 114 113
##   [256] 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113
##   [273] 112 112 112 112 112 112 112 112 112 112 112 112 112 112 112 112 112
##   [290] 112 112 112 111 111 111 111 111 111 111 111 111 111 111 111 111 111
##   [307] 111 111 111 111 111 111 110 110 110 110 110 110 110 110 110 110 109
##   [324] 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109
##   [341] 109 109 109 108 108 108 108 108 108 108 108 108 108 108 108 108 108
##   [358] 108 108 108 108 108 108 108 108 108 108 107 107 107 107 107 107 107
##   [375] 107 107 107 107 107 107 107 107 107 107 107 107 107 107 107 107 107
##   [392] 107 107 107 107 107 107 107 107 106 106 106 106 106 106 106 106 106
##   [409] 106 106 106 106 106 106 106 106 106 106 106 106 106 105 105 105 105
##   [426] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 104 104
##   [443] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 103 103 103
##   [460] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
##   [477] 103 103 103 103 103 103 103 103 103 103 103 103 103 102 102 102 102
##   [494] 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102
##   [511] 102 102 102 102 102 102 102 101 101 101 101 101 101 101 101 101 101
##   [528] 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101
##   [545] 101 101 101 101 101 100 100 100 100 100 100 100 100 100 100 100 100
##   [562] 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
##   [579] 100 100 100 100 100 100  99  99  99  99  99  99  99  99  99  99  99
##   [596]  99  99  99  99  99  99  99  99  99  99  99  99  99  99  99  99  99
##   [613]  99  99  99  99  99  99  99  98  98  98  98  98  98  98  98  98  98
##   [630]  98  98  98  98  98  98  98  98  98  98  98  98  98  98  98  98  98
##   [647]  98  98  98  98  98  98  98  98  98  98  98  98  97  97  97  97  97
##   [664]  97  97  97  97  97  97  97  97  97  97  97  97  97  97  97  97  97
##   [681]  97  97  97  97  97  96  96  96  96  96  96  96  96  96  96  96  96
##   [698]  96  96  96  96  96  96  96  96  96  96  96  96  96  96  96  96  96
##   [715]  96  96  96  96  96  96  96  96  95  95  95  95  95  95  95  95  95
##   [732]  95  95  95  95  95  95  95  95  95  95  95  95  95  95  95  95  95
##   [749]  95  95  95  95  94  94  94  94  94  94  94  94  94  94  94  94  94
##   [766]  94  94  94  94  94  94  94  94  94  94  94  94  94  94  94  94  94
##   [783]  94  94  94  94  94  93  93  93  93  93  93  93  93  93  93  93  93
##   [800]  93  93  93  93  93  93  93  93  93  93  93  93  93  93  93  93  93
##   [817]  93  93  93  93  93  93  92  92  92  92  92  92  92  92  92  92  92
##   [834]  92  92  92  92  92  92  92  92  92  92  92  92  92  92  92  92  92
##   [851]  91  91  91  91  91  91  91  91  91  91  91  91  91  91  91  91  91
##   [868]  91  91  91  91  91  91  91  91  91  91  91  91  91  91  91  91  91
##   [885]  91  91  90  90  90  90  90  90  90  90  90  90  90  90  90  90  90
##   [902]  90  90  90  90  90  90  90  90  90  90  90  90  90  90  90  90  90
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##   [936]  89  89  89  89  89  89  89  89  89  89  89  89  89  89  89  89  89
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##   [970]  88  88  88  88  88  88  88  88  88  88  88  88  88  88  88  88  88
##   [987]  88  88  88  88  88  88  88  88  88  88  88  88  88  88  88  88  88
##  [1004]  88  88  88  88  88  88  88  88  88  88  88  88  88  88  88  88  87
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##  [1786]  75  75  75  75  75  75  75  75  75  75  75  75  75  75  75  75  75
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##  [1973]  73  73  73  73  73  73  73  73  73  73  73  73  73  73  73  73  73
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##  [2041]  72  72  72  72  72  72  72  72  72  72  72  72  72  72  72  72  72
##  [2058]  72  72  72  72  72  72  72  72  72  72  72  72  72  72  72  72  72
##  [2075]  72  72  72  72  72  72  72  72  72  72  72  72  72  72  72  72  72
##  [2092]  72  72  72  72  71  71  71  71  71  71  71  71  71  71  71  71  71
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##  [4149]  54  54  54  54  54  54  54  54  54  54  54  54  54  54  54  54  54
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##  [7362]  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36
##  [7379]  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36
##  [7396]  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36
##  [7413]  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36
##  [7430]  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36
##  [7447]  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36
##  [7464]  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36  36
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##  [8416]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8433]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8450]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8467]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8484]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8501]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8518]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8535]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8552]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8569]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8586]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8603]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8620]  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
##  [8637]  31  31  31  31  31  31  31  31  31  30  30  30  30  30  30  30  30
##  [8654]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8671]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8688]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8705]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8722]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8739]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8756]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8773]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8790]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8807]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8824]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8841]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8858]  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
##  [8875]  30  30  30  30  30  30  30  30  30  30  30  30  30  29  29  29  29
##  [8892]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [8909]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [8926]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [8943]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [8960]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [8977]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [8994]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [9011]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [9028]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [9045]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [9062]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [9079]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [9096]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [9113]  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
##  [9130]  29  29  29  29  29  29  29  29  29  29  28  28  28  28  28  28  28
##  [9147]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9164]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9181]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9198]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9215]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9232]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9249]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9266]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9283]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9300]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9317]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9334]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9351]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9368]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9385]  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28  28
##  [9402]  28  28  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9419]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9436]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9453]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9470]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9487]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9504]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9521]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9538]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9555]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9572]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9589]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9606]  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27  27
##  [9623]  27  27  27  27  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9640]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9657]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9674]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9691]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9708]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9725]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9742]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9759]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9776]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9793]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9810]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9827]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9844]  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26  26
##  [9861]  26  26  26  26  26  26  26  26  26  26  26  26  25  25  25  25  25
##  [9878]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
##  [9895]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
##  [9912]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
##  [9929]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
##  [9946]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
##  [9963]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
##  [9980]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
##  [9997]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
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## [10048]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
## [10065]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
## [10082]  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25  25
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## [10167]  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24
## [10184]  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24
## [10201]  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24
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## [10252]  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24
## [10269]  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24
## [10286]  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24
## [10303]  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24
## [10320]  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24  24
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## [10354]  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23
## [10371]  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23
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## [10422]  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23
## [10439]  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23
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## [10490]  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23
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## [10524]  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23  23
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## [10660]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
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## [10711]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10728]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10745]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10762]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10779]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10796]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10813]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10830]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10847]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10864]  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22  22
## [10881]  22  22  22  22  22  22  22  22  22  21  21  21  21  21  21  21  21
## [10898]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [10915]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [10932]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [10949]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [10966]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [10983]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11000]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11017]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11034]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11051]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11068]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11085]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11102]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11119]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11136]  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21  21
## [11153]  21  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11170]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11187]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11204]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11221]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11238]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11255]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11272]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11289]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11306]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11323]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11340]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11357]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11374]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11391]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11408]  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20  20
## [11425]  20  20  20  20  20  20  20  20  20  20  19  19  19  19  19  19  19
## [11442]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11459]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11476]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11493]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11510]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11527]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11544]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11561]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11578]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11595]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11612]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11629]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11646]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11663]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11680]  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19  19
## [11697]  19  19  19  19  19  19  18  18  18  18  18  18  18  18  18  18  18
## [11714]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11731]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11748]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11765]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11782]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11799]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11816]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11833]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11850]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11867]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11884]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11901]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11918]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11935]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11952]  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18  18
## [11969]  18  18  18  18  18  18  18  18  18  17  17  17  17  17  17  17  17
## [11986]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12003]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12020]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12037]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12054]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12071]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12088]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12105]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12122]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12139]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12156]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12173]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12190]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12207]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12224]  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17  17
## [12241]  17  17  17  17  17  17  17  17  16  16  16  16  16  16  16  16  16
## [12258]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12275]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12292]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12309]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12326]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12343]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12360]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12377]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12394]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12411]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12428]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12445]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12462]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12479]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12496]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12513]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16
## [12530]  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  15  15
## [12547]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12564]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12581]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12598]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12615]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12632]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12649]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12666]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12683]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12700]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12717]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12734]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12751]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12768]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12785]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12802]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12819]  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15
## [12836]  15  15  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [12853]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [12870]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [12887]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [12904]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [12921]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [12938]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [12955]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [12972]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [12989]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [13006]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [13023]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [13040]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [13057]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [13074]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [13091]  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14  14
## [13108]  14  14  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13125]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13142]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13159]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13176]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13193]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13210]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13227]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13244]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13261]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13278]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13295]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13312]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13329]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13346]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13363]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13380]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13397]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13414]  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13
## [13431]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13448]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13465]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13482]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13499]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13516]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13533]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13550]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13567]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13584]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13601]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13618]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13635]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13652]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13669]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13686]  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12
## [13703]  12  12  12  12  12  12  12  12  11  11  11  11  11  11  11  11  11
## [13720]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13737]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13754]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13771]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13788]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13805]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13822]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13839]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13856]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13873]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13890]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13907]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13924]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13941]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13958]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13975]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
## [13992]  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  10
## [14009]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14026]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14043]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14060]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14077]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14094]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14111]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14128]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14145]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14162]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14179]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14196]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14213]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14230]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14247]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14264]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14281]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14298]  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10
## [14315]  10  10  10   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14332]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14349]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14366]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14383]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14400]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14417]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14434]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14451]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14468]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14485]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14502]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14519]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14536]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14553]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14570]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14587]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14604]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14621]   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9
## [14638]   9   9   9   9   9   9   9   9   8   8   8   8   8   8   8   8   8
## [14655]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14672]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14689]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14706]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14723]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14740]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14757]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14774]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14791]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14808]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14825]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14842]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14859]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14876]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14893]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14910]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14927]   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8
## [14944]   8   8   8   8   8   8   8   8   8   8   8   7   7   7   7   7   7
## [14961]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [14978]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [14995]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15012]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15029]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15046]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15063]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15080]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15097]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15114]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15131]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15148]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15165]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15182]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15199]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15216]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15233]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15250]   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7
## [15267]   7   7   7   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15284]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15301]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15318]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15335]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15352]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15369]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15386]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15403]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15420]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15437]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15454]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15471]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15488]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15505]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15522]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15539]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15556]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15573]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15590]   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6
## [15607]   6   6   6   6   6   6   6   6   6   5   5   5   5   5   5   5   5
## [15624]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15641]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15658]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15675]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15692]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15709]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15726]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15743]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15760]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15777]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15794]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15811]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15828]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15845]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15862]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15879]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15896]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15913]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15930]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15947]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15964]   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5
## [15981]   5   5   5   5   5   5   5   5   5   5   4   4   4   4   4   4   4
## [15998]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16015]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16032]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16049]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16066]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16083]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16100]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16117]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16134]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16151]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16168]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16185]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16202]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16219]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16236]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16253]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16270]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16287]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16304]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16321]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16338]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16355]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16372]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16389]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16406]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
## [16423]   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   3
## [16440]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16457]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16474]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16491]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16508]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16525]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16542]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16559]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16576]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16593]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16610]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16627]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16644]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16661]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16678]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16695]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16712]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16729]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16746]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16763]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16780]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16797]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16814]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16831]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16848]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16865]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16882]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16899]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16916]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16933]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16950]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16967]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [16984]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17001]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17018]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17035]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17052]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17069]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17086]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17103]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17120]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17137]   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## [17154]   3   3   3   3   3   3   3   3   3   3   3   3   3   2   2   2   2
## [17171]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17188]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17205]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17222]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17239]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17256]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17273]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17290]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17307]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17324]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17341]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17358]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17375]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17392]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17409]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17426]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17443]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17460]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17477]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17494]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17511]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17528]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17545]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17562]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17579]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17596]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17613]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17630]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17647]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17664]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17681]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17698]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17715]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17732]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17749]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17766]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17783]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17800]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17817]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17834]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17851]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17868]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17885]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17902]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17919]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17936]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17953]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17970]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [17987]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18004]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18021]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18038]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18055]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18072]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18089]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18106]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18123]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18140]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18157]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18174]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18191]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18208]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18225]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18242]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18259]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18276]   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2
## [18293]   2   2   2   2   2   2   2   2   2   2   2   2   2   1   1   1   1
## [18310]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18327]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18344]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18361]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18378]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18395]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18412]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18429]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18446]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18463]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18480]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18497]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18514]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18531]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18548]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18565]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18582]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18599]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18616]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18633]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18650]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18667]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18684]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18701]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18718]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18735]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18752]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18769]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18786]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18803]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18820]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18837]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18854]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18871]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18888]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18905]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18922]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18939]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18956]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18973]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [18990]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [19007]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [19024]   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
## [19041]   1   1   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19058]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19075]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19092]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19109]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19126]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19143]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19160]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19177]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19194]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19211]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19228]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19245]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19262]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19279]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19296]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19313]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19330]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19347]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19364]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19381]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19398]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19415]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19432]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19449]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19466]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19483]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19500]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19517]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19534]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19551]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19568]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19585]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19602]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19619]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19636]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19653]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19670]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19687]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19704]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## [19721]   0   0   0   0   0   0   0   0

head(r)

##            FECHA HORA estaciones valor pollutant
## 90657 2010-05-08    9        MER    14        O3
## 90658 2010-05-08   10        MER    35        O3
## 90659 2010-05-08   11        MER    59        O3
## 90660 2010-05-08   12        MER    75        O3
## 90661 2010-05-08   13        MER   101        O3
## 90662 2010-05-08   14        MER   108        O3

maxo3 <- ggplot(r, aes(x=FECHA, y=valor, color = estaciones, alpha=0.05)) + geom_line() + theme_bw()
maxo3

dias_max_o3 <- read.csv("/Users/Vanessa/Desktop/CSV/pollutants_dias_de_max_o3.csv")
dias_max_o3 <- dias_max_o3[,-5]
dias_max_o3 <- dias_max_o3[dias_max_o3$VALOR >= 0, ]
p_diasmax_o3 <- ggplot(dias_max_o3, aes(x=FECHA, y=VALOR, color = POLLUTANT, alpha = 0.05)) + geom_point()+ facet_grid(ESTACION ~ .) + theme_bw()
p_diasmax_o3

p_diasmax_o3_2 <- ggplot(dias_max_o3, aes(x=FECHA, y=VALOR, color = ESTACION, alpha = 0.05)) + geom_point()+ facet_grid(POLLUTANT ~ .) + theme_bw()
p_diasmax_o3_2

MARZO-ABRIL

# no_feb_29 <- !grepl("02-29", rama$FECHA)
# rama_sin29feb <- rama[-no_feb_29, ]
# 
# mar <- c("03-01","03-02","03-03","03-04","03-05","03-06","03-07","03-08","03-09","03-10","03-11","03-12","03-13","03-14","03-15","03-16","03-17","03-18","03-19","03-20","03-01","03-21","03-22","03-23","03-24","03-25","03-26","03-27","03-28","03-29", "03-30","03-31")
# marz <- grep(mar, rama_sin29feb$FECHA)
# 

date1 <- as.Date("2010-03-01")
date2 <- as.Date("2010-05-31")

dates <- seq(date1, date2, by = "days")
mar_may <- rama[rama$FECHA == dates, ]

## Warning in `==.default`(rama$FECHA, dates): longitud de objeto mayor no es
## m'ultiplo de la longitud de uno menor

time <- ggplot(mar_may, aes(x=FECHA, y=valor, color=pollutant, alpha = 0.05)) + geom_line() + theme_bw()
time

Ejemplo de compresión de Serie de Tiempo

# Library
# library(dygraphs)
# library(xts)          # To make the convertion data-frame / xts format
# library(dplyr)
# library(lubridate)
#  
# # Read the data (hosted on the gallery website)
# data=read.table("https://python-graph-gallery.com/wp-content/uploads/bike.csv", header=T, sep=",") %>% head(300)
# str(data)
#  
# # Since my time is currently a factor, I have to convert it to a date-time format!
# data$datetime = ymd_hms(data$datetime)
#  
# # Then you can create the xts format, and thus use dygraph
# don=xts(x = data$count, order.by = data$datetime)
# widg = dygraph(don) %>%
#   dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors="#D8AE5A") %>%
#   dyRangeSelector() %>%
#   dyCrosshair(direction = "vertical") %>%
#   dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE)  %>%
#   dyRoller(rollPeriod = 1)
# 
# widg

D-V-J

# datos tiempo real en IMECA
df <- get_latest_data()
head(df)
df2 <- get_station_data("HORARIOS", "O3", 2010:2011)
head(df2)

# df3 <- df2 %>% group_by(date, station_code) %>% summarise(max_value = max(value, na.rm = TRUE), , mean_value = mean(value, na.rm = TRUE))
df3 <- df2 %>% group_by(date) %>% summarise(max_value = max(value, na.rm = TRUE), 
                                            mean_value = mean(value, na.rm = TRUE), 
                                            min_value = min(value, na.rm = TRUE)) %>% 
               gather(type, value, max_value:min_value, -date)

# ggplot(df3, aes(x = date, y = max_value, color = station_code)) + geom_line()  + theme_bw()
ggplot(df3, aes(x = date, y = value, color = as.factor(type))) + geom_line() + theme_bw()